Review of observational medical studies and measures of association

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Review of observational medical studies and
measures of association
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• Coffee Chronicles
• BY MELISSA AUGUST, ANN MARIE BONARDI, VAL
  CASTRONOVO, MATTHEW
• JOE'S BLOWS Last week researchers reported that
  coffee might help prevent Parkinson's disease. So
  is the caffeine bean good for you or not? Over
  the years, studies haven't exactly been clear

• According to scientists, too much coffee may
  cause...
• 1986 --phobias, --panic attacks
• 1990 --heart attacks, --stress, --osteoporosis
• 1991 -underweight babies, --hypertension
• 1992 --higher cholesterol
• 1993 --miscarriages
• 1994 --intensified stress
• 1995 --delayed conception
• But scientists say coffee also may help
  prevent...
• 1988 --asthma
• 1990 --colon and rectal cancer,...
• 2004Type II Diabetes (6 cups per day!)
• 2006alcohol-induced liver damage
• 2007skin cancer

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Medical Studies
The General Idea
Evaluate whether a risk factor (or preventative
factor) increases (or decreases) your risk for an
outcome (usually disease, death or intermediary
to disease).
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Observational vs. Experimental Studies
Observational studies the population is
observed without any interference by the
investigator
Experimental studies the investigator tries to
control the environment in which the hypothesis
is tested (the randomized, double-blind clinical
trial is the gold standard)
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Confounding A major problem for observational
studies
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Confounding Example
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Confounding example
50 of cases are drinkers, but only 25 of
controls are drinkers. Therefore, it appears that
drinking is strongly associated with lung cancer.
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Confounding example
Smoker
Among smokers, 45/7560 of lung cancer cases
drink and 15/2560 of controls drink.
75
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Non-smoker
Among non-smokers 5/2520 of lung cancer cases
drink and 35/17520 of controls drink.
25
175
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Why Observational Studies?

• Cheaper
• Faster
• Can examine long-term effects
• Hypothesis-generating
• Sometimes, experimental studies are not ethical
  (e.g., randomizing subjects to smoke)

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What is risk for a biostatistician?

• Risk Probability of developing a disease or
  other adverse outcome (over a defined time
  period)
• In Symbols P(D)
• Conditional Risk Risk of developing a disease
  given a particular exposure
• In Symbols P(D/E)
• Odds Probability of developing a disease
  divided by the probability of not developing it
• In Symbols P(D)/P(D)

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Possible Observational Study Designs

• Cross-sectional studies
• Cohort studies
• Case-control studies

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Cross-Sectional (Prevalence) Studies

• Measure disease and exposure on a random sample
  of the population of interest. Are they
  associated?
• Marginal probabilities of exposure AND disease
  are valid, but only measures association at a
  single time point.

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The 2x2 Table
N
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Example cross-sectional study

• Relationship between atherosclerosis and
  late-life depression (Tiemeier et al. Arch Gen
  Psychiatry, 2004).
• Methods Researchers measured the prevalence of
  coronary artery calcification (atherosclerosis)
  and the prevalence of depressive symptoms in a
  large cohort of elderly men and women in
  Rotterdam (n1920).

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Example cross-sectional study
P(D) Prevalence of depression (sub-thresshold
or depressive disorder) (20131291116)/1920
4.2
P(E) Prevalence of atherosclerosis (coronary
calcification 500) (5111216)/1920 28.1
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The 2x2 table
P(depression) 81/1920 4.2
P(atherosclerosis) 539/1920 28.1
P(depression/atherosclerosis) 28/539 5.2
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Difference of proportions Z-test
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Cause and effect?
depression in elderly
atherosclerosis
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Confounding?
depression in elderly
atherosclerosis
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Cross-Sectional Studies

• Advantages
• cheap and easy
• generalizable
• good for characteristics that (generally) dont
  change like genes or gender
• Disadvantages
• difficult to determine cause and effect
• problematic for rare diseases and exposures

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2. Cohort studies

• Sample on exposure status and track disease
  development (for rare exposures)
• Marginal probabilities (and rates) of developing
  disease for exposure groups are valid.

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Example The Framingham Heart Study

• The Framingham Heart Study was established in
  1948, when 5209 residents of Framingham, Mass,
  aged 28 to 62 years, were enrolled in a
  prospective epidemiologic cohort study.
• Health and lifestyle factors were measured (blood
  pressure, weight, exercise, etc.).
• Interim cardiovascular events were ascertained
  from medical histories, physical examinations,
  ECGs, and review of interim medical record.

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Example 2 Johns Hopkins Precursors
Study(medical students 1948 through 1964)
http//www.jhu.edu/jhumag/0601web/study.html
From the John Hopkins Magazine website (URL
above).
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Cohort Studies
Disease
Disease-free
Target population
Disease
Disease-free
TIME
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The Risk Ratio, or Relative Risk (RR)
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Hypothetical Data

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Advantages/LimitationsCohort Studies

• Advantages
• Allows you to measure true rates and risks of
  disease for the exposed and the unexposed groups.
• Temporality is correct (easier to infer cause and
  effect).
• Can be used to study multiple outcomes.
• Prevents bias in the ascertainment of exposure
  that may occur after a person develops a disease.
• Disadvantages
• Can be lengthy and costly! 60 years for
  Framingham.
• Loss to follow-up is a problem (especially if
  non-random).
• Selection Bias Participation may be associated
  with exposure status for some exposures

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Case-Control Studies

• Sample on disease status and ask retrospectively
  about exposures (for rare diseases)
• Marginal probabilities of exposure for cases and
  controls are valid.
• Doesnt require knowledge of the absolute risks
  of disease
• For rare diseases, can approximate relative risk

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Case-Control Studies
Exposed in past

• Disease
• (Cases)

Not exposed
Target population
Exposed
No Disease (Controls)
Not Exposed
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Example the AIDS epidemic in the early 1980s

• Early, case-control studies among AIDS cases and
  matched controls indicated that AIDS was
  transmitted by sexual contact or blood products.
• In 1982, an early case-control study matched AIDS
  cases to controls and found a positive
  association between amyl nitrites (poppers) and
  AIDS odds ratio of 8.6 (Marmor et al. 1982).
  This is an example of confounding.

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Case-Control Studies in History

• In 1843, Guy compared occupations of men with
  pulmonary consumption to those of men with other
  diseases (Lilienfeld and Lilienfeld 1979).
• Case-control studies identified associations
  between lip cancer and pipe smoking (Broders
  1920), breast cancer and reproductive history
  (Lane-Claypon 1926) and between oral cancer and
  pipe smoking (Lombard and Doering 1928). All
  rare diseases.
• Case-control studies identified an association
  between smoking and lung cancer in the 1950s.

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Case-control example

• A study of the relation between body mass index
  and the incidence of age-related macular
  degeneration (Moeini et al. Br. J. Ophthalmol,
  2005).
• Methods Researchers compared 50 Iranian patients
  with confirmed age-related macular degeneration
  and 80 control subjects with respect to BMI,
  smoking habits, hypertension, and diabetes. The
  researchers were specifically interested in the
  relationship of BMI to age-related macular
  degeneration.

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Results
Table 2  Comparison of body mass index (BMI) in
case and control groups

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Corresponding 2x2 Table
50
80
What is the risk ratio here? Tricky There is no
risk ratio, because we cannot calculate the risk
of disease!!
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The odds ratio

• We cannot calculate a risk ratio from a
  case-control study.
• BUT, we can calculate a measure called the odds
  ratio

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Odds vs. Risk
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Note An odds is always higher than its
corresponding probability, unless the probability
is 100.
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The Odds Ratio (OR)
abcases
cdcontrols
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The Odds Ratio (OR)
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Proof via Bayes Rule (optional)


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The Odds Ratio (OR)
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The Odds Ratio (OR)
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The Odds Ratio (OR)
Can be interpreted as Overweight people have a
43 decrease in their ODDS of age-related macular
degeneration. (not statistically significant
here)
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The odds ratio is a good approximation of the
risk ratio if the disease is rare.
If the disease is rare (affecting population), then
WHY? If the disease is rare, the probability of
it NOT happening is close to 1, and the odds is
close to the risk. Eg
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The rare disease assumption
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The odds ratio vs. the risk ratio
Rare Outcome
1.0 (null)
Common Outcome
1.0 (null)
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When is the OR is a good approximation of the RR?
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Advantages/LimitationsCase-control studies

• Advantages
• Cheap and fast
• Efficient for rare diseases
• Disadvantages
• Getting comparable controls is often tricky
• Temporality is a problem (did risk factor cause
  disease or disease cause risk factor?
• Recall bias

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Inferences about the odds ratio
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Properties of the OR (simulation)
(50 cases/50 controls/20 exposed)
If the Odds Ratio1.0 then with 50 cases and 50
controls, of whom 20 are exposed, this is the
expected variability of the sample OR?note the
right skew
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Properties of the lnOR
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Hypothetical Data
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30
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When can the OR mislead?
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ExampleDoes dementia predict death?

• Dementia The leading predictor of death in a
  defined elderly population. Neurology 2004 62
  1156-1162
• Among patients with dementia 291/355 (82) died
• Among patients without dementia 947/4328 (22)
  died

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Dementia study

• Authors report OR 16.23 (12.27, 21.48)
• But the RR 3.72
• Fortunately, they do not dwell on the OR, but it
  could mislead if not interpreted correctly